Find the minimal data type of an array-like in Python

The numpy.min_scalar_type() method finds the minimal data type that can hold a given value. For scalars, it returns the data type with the smallest size that can store the value. For arrays, it returns the array's dtype unmodified. Floating point values are not demoted to integers, and complex values are not demoted to floats.

Syntax

numpy.min_scalar_type(a)

Parameters

a ? The value whose minimal data type is to be found. Can be a scalar or array-like.

Basic Examples

Let's start with simple scalar values to understand how the function determines minimal data types ?

import numpy as np

# Integer values
print("Integer examples:")
print("Value 10:", np.min_scalar_type(10))
print("Value 300:", np.min_scalar_type(300))
print("Value -50:", np.min_scalar_type(-50))

Integer examples:
Value 10: uint8
Value 300: uint16
Value -50: int8

Float and Complex Values

For floating point numbers, the function chooses the smallest float type that can represent the value ?

import numpy as np

# Float values
print("Float examples:")
print("Value 3.14:", np.min_scalar_type(3.14))
print("Value 1e-5:", np.min_scalar_type(1e-5))
print("Large float 6.5e100:", np.min_scalar_type(6.5e100))

# Complex values
print("\nComplex examples:")
print("Value 3+4j:", np.min_scalar_type(3+4j))

Float examples:
Value 3.14: float16
Value 1e-5: float16
Large float 6.5e100: float64

Complex examples:
Value 3+4j: complex64

Array Examples

When working with NumPy arrays, the function analyzes the array's values to find the minimal suitable type ?

import numpy as np

print("Array examples:")

# Arrays with different dtypes
arr1 = np.arange(4, dtype='f8')
print("arange(4, dtype='f8'):", np.min_scalar_type(arr1))

arr2 = np.array(280, 'i1')
print("array(280, 'i1'):", np.min_scalar_type(arr2))

arr3 = np.array(80, 'u1')
print("array(80, 'u1'):", np.min_scalar_type(arr3))

arr4 = np.array(300.7, np.float32)
print("array(300.7, float32):", np.min_scalar_type(arr4))

Array examples:
arange(4, dtype='f8'): float64
array(280, 'i1'): uint8
array(80, 'u1'): uint8
array(300.7, float32): float16

Data Type Size Optimization

This table shows how different values are optimized to minimal data types ?

Conclusion

The numpy.min_scalar_type() function helps optimize memory usage by finding the smallest data type that can hold a given value. This is particularly useful for memory-efficient array operations in scientific computing.